{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Riskfolio-Lib Tutorial: \n",
    "<br>__[Financionerioncios](https://financioneroncios.wordpress.com)__\n",
    "<br>__[Orenji](https://www.orenj-i.net)__\n",
    "<br>__[Riskfolio-Lib](https://riskfolio-lib.readthedocs.io/en/latest/)__\n",
    "<br>__[Dany Cajas](https://www.linkedin.com/in/dany-cajas/)__\n",
    "<a href='https://ko-fi.com/B0B833SXD' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://cdn.ko-fi.com/cdn/kofi1.png?v=2' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a> \n",
    "\n",
    "## Tutorial 22: Logarithmic Mean Risk Optimization (Kelly Criterion)\n",
    "\n",
    "## 1. Downloading the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  26 of 26 completed\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import yfinance as yf\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.options.display.float_format = '{:.4%}'.format\n",
    "\n",
    "# Date range\n",
    "start = '2000-01-01'\n",
    "end = '2019-12-31'\n",
    "\n",
    "# Tickers of assets\n",
    "assets = ['AIG', 'AKAM', 'AMT', 'APA', 'BA', 'BAX', 'BKNG',\n",
    "          'BMY', 'CMCSA', 'CNP', 'CPB', 'DE', 'MO', 'MSFT', 'NI',\n",
    "          'NKTR', 'NTAP', 'PCAR', 'PSA', 'REGN', 'SBAC', 'SEE', 'T',\n",
    "          'TGT', 'TMO', 'TTWO']\n",
    "\n",
    "assets.sort()\n",
    "\n",
    "# Downloading data\n",
    "data = yf.download(assets, start = start, end = end)\n",
    "data = data.loc[:,('Adj Close', slice(None))]\n",
    "data.columns = assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(239, 26)\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AIG</th>\n",
       "      <th>AKAM</th>\n",
       "      <th>AMT</th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BKNG</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>...</th>\n",
       "      <th>NTAP</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>REGN</th>\n",
       "      <th>SBAC</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TTWO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-02-29</th>\n",
       "      <td>-15.2695%</td>\n",
       "      <td>4.8670%</td>\n",
       "      <td>37.2823%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>-16.7100%</td>\n",
       "      <td>-14.6772%</td>\n",
       "      <td>-3.5560%</td>\n",
       "      <td>-13.5849%</td>\n",
       "      <td>-7.2464%</td>\n",
       "      <td>-8.3352%</td>\n",
       "      <td>...</td>\n",
       "      <td>88.0448%</td>\n",
       "      <td>4.6442%</td>\n",
       "      <td>-2.7548%</td>\n",
       "      <td>358.8832%</td>\n",
       "      <td>33.6083%</td>\n",
       "      <td>-11.4699%</td>\n",
       "      <td>-11.9533%</td>\n",
       "      <td>-10.2961%</td>\n",
       "      <td>-9.7473%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-03-31</th>\n",
       "      <td>23.8864%</td>\n",
       "      <td>-38.4450%</td>\n",
       "      <td>0.2538%</td>\n",
       "      <td>36.5170%</td>\n",
       "      <td>2.3689%</td>\n",
       "      <td>15.0229%</td>\n",
       "      <td>43.0168%</td>\n",
       "      <td>-0.2183%</td>\n",
       "      <td>3.1250%</td>\n",
       "      <td>14.5896%</td>\n",
       "      <td>...</td>\n",
       "      <td>-12.3179%</td>\n",
       "      <td>16.1105%</td>\n",
       "      <td>-3.8081%</td>\n",
       "      <td>-47.6770%</td>\n",
       "      <td>8.6419%</td>\n",
       "      <td>9.3082%</td>\n",
       "      <td>11.5894%</td>\n",
       "      <td>26.6949%</td>\n",
       "      <td>30.4000%</td>\n",
       "      <td>6.5327%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-04-30</th>\n",
       "      <td>0.1712%</td>\n",
       "      <td>-38.5154%</td>\n",
       "      <td>-5.6962%</td>\n",
       "      <td>-2.6382%</td>\n",
       "      <td>4.9586%</td>\n",
       "      <td>8.5879%</td>\n",
       "      <td>-20.9375%</td>\n",
       "      <td>-7.8650%</td>\n",
       "      <td>-5.4545%</td>\n",
       "      <td>12.9974%</td>\n",
       "      <td>...</td>\n",
       "      <td>-10.6496%</td>\n",
       "      <td>-4.8751%</td>\n",
       "      <td>6.5476%</td>\n",
       "      <td>-3.3827%</td>\n",
       "      <td>-7.6704%</td>\n",
       "      <td>2.4165%</td>\n",
       "      <td>4.7667%</td>\n",
       "      <td>-10.9532%</td>\n",
       "      <td>-4.9080%</td>\n",
       "      <td>-27.3585%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-05-31</th>\n",
       "      <td>2.6662%</td>\n",
       "      <td>-32.4905%</td>\n",
       "      <td>-20.2684%</td>\n",
       "      <td>25.6130%</td>\n",
       "      <td>-1.2137%</td>\n",
       "      <td>2.1113%</td>\n",
       "      <td>-39.7233%</td>\n",
       "      <td>5.0060%</td>\n",
       "      <td>-3.6859%</td>\n",
       "      <td>8.7704%</td>\n",
       "      <td>...</td>\n",
       "      <td>-12.6796%</td>\n",
       "      <td>-11.3612%</td>\n",
       "      <td>-0.2794%</td>\n",
       "      <td>-28.6652%</td>\n",
       "      <td>-8.3077%</td>\n",
       "      <td>0.6742%</td>\n",
       "      <td>-0.2853%</td>\n",
       "      <td>-5.6896%</td>\n",
       "      <td>-4.1935%</td>\n",
       "      <td>-5.8442%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-06-30</th>\n",
       "      <td>4.3865%</td>\n",
       "      <td>77.8792%</td>\n",
       "      <td>12.2895%</td>\n",
       "      <td>-3.3385%</td>\n",
       "      <td>7.0400%</td>\n",
       "      <td>5.7331%</td>\n",
       "      <td>-0.3688%</td>\n",
       "      <td>5.7889%</td>\n",
       "      <td>3.4941%</td>\n",
       "      <td>3.5011%</td>\n",
       "      <td>...</td>\n",
       "      <td>24.6854%</td>\n",
       "      <td>-5.2239%</td>\n",
       "      <td>6.1300%</td>\n",
       "      <td>46.3190%</td>\n",
       "      <td>39.4295%</td>\n",
       "      <td>-6.4732%</td>\n",
       "      <td>0.7153%</td>\n",
       "      <td>-7.4776%</td>\n",
       "      <td>13.4679%</td>\n",
       "      <td>33.7931%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 AIG      AKAM       AMT      APA        BA       BAX  \\\n",
       "Date                                                                    \n",
       "2000-02-29 -15.2695%   4.8670%  37.2823%  0.0000% -16.7100% -14.6772%   \n",
       "2000-03-31  23.8864% -38.4450%   0.2538% 36.5170%   2.3689%  15.0229%   \n",
       "2000-04-30   0.1712% -38.5154%  -5.6962% -2.6382%   4.9586%   8.5879%   \n",
       "2000-05-31   2.6662% -32.4905% -20.2684% 25.6130%  -1.2137%   2.1113%   \n",
       "2000-06-30   4.3865%  77.8792%  12.2895% -3.3385%   7.0400%   5.7331%   \n",
       "\n",
       "                BKNG       BMY    CMCSA      CNP  ...      NTAP      PCAR  \\\n",
       "Date                                              ...                       \n",
       "2000-02-29  -3.5560% -13.5849% -7.2464% -8.3352%  ...  88.0448%   4.6442%   \n",
       "2000-03-31  43.0168%  -0.2183%  3.1250% 14.5896%  ... -12.3179%  16.1105%   \n",
       "2000-04-30 -20.9375%  -7.8650% -5.4545% 12.9974%  ... -10.6496%  -4.8751%   \n",
       "2000-05-31 -39.7233%   5.0060% -3.6859%  8.7704%  ... -12.6796% -11.3612%   \n",
       "2000-06-30  -0.3688%   5.7889%  3.4941%  3.5011%  ...  24.6854%  -5.2239%   \n",
       "\n",
       "                PSA      REGN     SBAC       SEE         T       TGT      TMO  \\\n",
       "Date                                                                            \n",
       "2000-02-29 -2.7548% 358.8832% 33.6083% -11.4699% -11.9533% -10.2961% -9.7473%   \n",
       "2000-03-31 -3.8081% -47.6770%  8.6419%   9.3082%  11.5894%  26.6949% 30.4000%   \n",
       "2000-04-30  6.5476%  -3.3827% -7.6704%   2.4165%   4.7667% -10.9532% -4.9080%   \n",
       "2000-05-31 -0.2794% -28.6652% -8.3077%   0.6742%  -0.2853%  -5.6896% -4.1935%   \n",
       "2000-06-30  6.1300%  46.3190% 39.4295%  -6.4732%   0.7153%  -7.4776% 13.4679%   \n",
       "\n",
       "                TTWO  \n",
       "Date                  \n",
       "2000-02-29   0.0000%  \n",
       "2000-03-31   6.5327%  \n",
       "2000-04-30 -27.3585%  \n",
       "2000-05-31  -5.8442%  \n",
       "2000-06-30  33.7931%  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating returns\n",
    "\n",
    "#Y = data[assets].pct_change().dropna()\n",
    "Y = data[assets].copy()\n",
    "Y = Y.resample('M').last().pct_change().dropna()\n",
    "print(Y.shape)\n",
    "display(Y.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Estimating Logarithmic Mean Variance Portfolios\n",
    "\n",
    "### 2.1 Calculating the portfolio that maximizes Risk Adjusted Return."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Arithmetic</th>\n",
       "      <th>Log Approx</th>\n",
       "      <th>Log Exact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AIG</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AKAM</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0001%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APA</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>1.4588%</td>\n",
       "      <td>1.4078%</td>\n",
       "      <td>1.3887%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAX</th>\n",
       "      <td>1.3214%</td>\n",
       "      <td>1.8791%</td>\n",
       "      <td>1.8891%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BKNG</th>\n",
       "      <td>2.6984%</td>\n",
       "      <td>2.5498%</td>\n",
       "      <td>2.5603%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMY</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0792%</td>\n",
       "      <td>0.1514%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMCSA</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNP</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.3561%</td>\n",
       "      <td>0.4178%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CPB</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>2.7384%</td>\n",
       "      <td>2.7192%</td>\n",
       "      <td>2.6556%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MO</th>\n",
       "      <td>28.0287%</td>\n",
       "      <td>27.3138%</td>\n",
       "      <td>27.3190%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0001%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NI</th>\n",
       "      <td>6.3847%</td>\n",
       "      <td>6.4490%</td>\n",
       "      <td>6.4570%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NKTR</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTAP</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCAR</th>\n",
       "      <td>7.1819%</td>\n",
       "      <td>6.9750%</td>\n",
       "      <td>7.0007%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSA</th>\n",
       "      <td>30.2594%</td>\n",
       "      <td>30.1042%</td>\n",
       "      <td>30.0408%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REGN</th>\n",
       "      <td>2.1683%</td>\n",
       "      <td>2.0784%</td>\n",
       "      <td>2.0747%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SBAC</th>\n",
       "      <td>2.6696%</td>\n",
       "      <td>2.4625%</td>\n",
       "      <td>2.4858%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEE</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.7714%</td>\n",
       "      <td>0.8460%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TGT</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0001%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TMO</th>\n",
       "      <td>10.1667%</td>\n",
       "      <td>10.0819%</td>\n",
       "      <td>9.9504%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTWO</th>\n",
       "      <td>4.9237%</td>\n",
       "      <td>4.7725%</td>\n",
       "      <td>4.7626%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Arithmetic  Log Approx  Log Exact\n",
       "AIG       0.0000%     0.0000%    0.0000%\n",
       "AKAM      0.0000%     0.0000%    0.0000%\n",
       "AMT       0.0000%     0.0001%    0.0000%\n",
       "APA       0.0000%     0.0000%    0.0000%\n",
       "BA        1.4588%     1.4078%    1.3887%\n",
       "BAX       1.3214%     1.8791%    1.8891%\n",
       "BKNG      2.6984%     2.5498%    2.5603%\n",
       "BMY       0.0000%     0.0792%    0.1514%\n",
       "CMCSA     0.0000%     0.0000%    0.0000%\n",
       "CNP       0.0000%     0.3561%    0.4178%\n",
       "CPB       0.0000%     0.0000%    0.0000%\n",
       "DE        2.7384%     2.7192%    2.6556%\n",
       "MO       28.0287%    27.3138%   27.3190%\n",
       "MSFT      0.0000%     0.0001%    0.0000%\n",
       "NI        6.3847%     6.4490%    6.4570%\n",
       "NKTR      0.0000%     0.0000%    0.0000%\n",
       "NTAP      0.0000%     0.0000%    0.0000%\n",
       "PCAR      7.1819%     6.9750%    7.0007%\n",
       "PSA      30.2594%    30.1042%   30.0408%\n",
       "REGN      2.1683%     2.0784%    2.0747%\n",
       "SBAC      2.6696%     2.4625%    2.4858%\n",
       "SEE       0.0000%     0.0000%    0.0000%\n",
       "T         0.0000%     0.7714%    0.8460%\n",
       "TGT       0.0000%     0.0001%    0.0000%\n",
       "TMO      10.1667%    10.0819%    9.9504%\n",
       "TTWO      4.9237%     4.7725%    4.7626%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import riskfolio as rp\n",
    "\n",
    "# Building the portfolio object\n",
    "port = rp.Portfolio(returns=Y)\n",
    "\n",
    "# Calculating optimal portfolio\n",
    "\n",
    "# Select method and estimate input parameters:\n",
    "\n",
    "method_mu='hist' # Method to estimate expected returns based on historical data.\n",
    "method_cov='hist' # Method to estimate covariance matrix based on historical data.\n",
    "\n",
    "port.assets_stats(method_mu=method_mu, method_cov=method_cov)\n",
    "\n",
    "# Estimate optimal portfolio:\n",
    "\n",
    "port.solvers = ['MOSEK']\n",
    "model='Classic' # Could be Classic (historical), BL (Black Litterman) or FM (Factor Model)\n",
    "rm = 'MV' # Risk measure used, this time will be variance\n",
    "obj = 'Sharpe' # Objective function, could be MinRisk, MaxRet, Utility or Sharpe\n",
    "hist = True # Use historical scenarios for risk measures that depend on scenarios\n",
    "rf = 0 # Risk free rate\n",
    "l = 0 # Risk aversion factor, only useful when obj is 'Utility'\n",
    "\n",
    "w_1 = port.optimization(model=model, rm=rm, obj=obj, kelly=False, rf=rf, l=l, hist=hist)\n",
    "w_2 = port.optimization(model=model, rm=rm, obj=obj, kelly='approx', rf=rf, l=l, hist=hist)\n",
    "w_3 = port.optimization(model=model, rm=rm, obj=obj, kelly='exact', rf=rf, l=l, hist=hist)\n",
    "\n",
    "w = pd.concat([w_1, w_2, w_3], axis=1)\n",
    "w.columns = ['Arithmetic', 'Log Approx', 'Log Exact']\n",
    "\n",
    "display(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14,6))\n",
    "w.plot(kind='bar', ax = ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Risk Adjusted Return:\n",
      "Arithmetic 1.2708633284065216\n",
      "Log Approx 1.271277509792363\n",
      "Log Exact 1.2712853994711972\n"
     ]
    }
   ],
   "source": [
    "returns = port.returns\n",
    "cov = port.cov\n",
    "\n",
    "y = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_1.to_numpy()))\n",
    "x = rp.Sharpe_Risk(w_1, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05)\n",
    "print(\"Risk Adjusted Return:\")\n",
    "print(\"Arithmetic\", (y/x).item() * 12**0.5)\n",
    "\n",
    "y = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_2.to_numpy()))\n",
    "x = rp.Sharpe_Risk(w_2, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05)\n",
    "print(\"Log Approx\", (y/x).item() * 12**0.5)\n",
    "\n",
    "y = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_3.to_numpy()))\n",
    "x = rp.Sharpe_Risk(w_3, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05)\n",
    "print(\"Log Exact\", (y/x).item() * 12**0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Calculate efficient frontier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AIG</th>\n",
       "      <th>AKAM</th>\n",
       "      <th>AMT</th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BKNG</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>...</th>\n",
       "      <th>NTAP</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>REGN</th>\n",
       "      <th>SBAC</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TTWO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.9732%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.5649%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>8.8956%</td>\n",
       "      <td>4.2389%</td>\n",
       "      <td>4.6817%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0047%</td>\n",
       "      <td>21.9645%</td>\n",
       "      <td>0.9348%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>8.7338%</td>\n",
       "      <td>2.6977%</td>\n",
       "      <td>1.2639%</td>\n",
       "      <td>1.9033%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.4694%</td>\n",
       "      <td>4.2742%</td>\n",
       "      <td>1.5116%</td>\n",
       "      <td>4.3741%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.8896%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.8768%</td>\n",
       "      <td>27.7611%</td>\n",
       "      <td>1.6011%</td>\n",
       "      <td>0.9927%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.9223%</td>\n",
       "      <td>0.0310%</td>\n",
       "      <td>7.6685%</td>\n",
       "      <td>3.8109%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.0669%</td>\n",
       "      <td>2.6833%</td>\n",
       "      <td>2.3190%</td>\n",
       "      <td>1.5048%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.1507%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.5123%</td>\n",
       "      <td>29.6692%</td>\n",
       "      <td>1.9032%</td>\n",
       "      <td>2.1296%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.3494%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>9.6204%</td>\n",
       "      <td>4.5798%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.1690%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.1125%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>7.3735%</td>\n",
       "      <td>30.3568%</td>\n",
       "      <td>2.4822%</td>\n",
       "      <td>3.3581%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>9.7291%</td>\n",
       "      <td>5.7723%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0269%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.9908%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>7.5013%</td>\n",
       "      <td>30.0451%</td>\n",
       "      <td>3.1738%</td>\n",
       "      <td>4.7584%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>8.3937%</td>\n",
       "      <td>7.7205%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      AIG    AKAM     AMT     APA      BA     BAX    BKNG     BMY   CMCSA  \\\n",
       "0 0.0000% 0.0000% 0.0000% 0.9732% 0.0000% 6.5649% 0.0000% 8.8956% 4.2389%   \n",
       "1 0.0000% 0.0000% 0.0000% 0.0000% 0.4694% 4.2742% 1.5116% 4.3741% 0.0000%   \n",
       "2 0.0000% 0.0000% 0.0000% 0.0000% 1.0669% 2.6833% 2.3190% 1.5048% 0.0000%   \n",
       "3 0.0000% 0.0000% 0.0000% 0.0000% 1.1690% 0.0000% 3.1125% 0.0000% 0.0000%   \n",
       "4 0.0000% 0.0000% 0.0000% 0.0000% 0.0269% 0.0000% 3.9908% 0.0000% 0.0000%   \n",
       "\n",
       "      CNP  ...    NTAP    PCAR      PSA    REGN    SBAC     SEE       T  \\\n",
       "0 4.6817%  ... 0.0000% 0.0047% 21.9645% 0.9348% 0.0000% 0.0000% 8.7338%   \n",
       "1 2.8896%  ... 0.0000% 4.8768% 27.7611% 1.6011% 0.9927% 0.0000% 4.9223%   \n",
       "2 1.1507%  ... 0.0000% 6.5123% 29.6692% 1.9032% 2.1296% 0.0000% 2.3494%   \n",
       "3 0.0000%  ... 0.0000% 7.3735% 30.3568% 2.4822% 3.3581% 0.0000% 0.0000%   \n",
       "4 0.0000%  ... 0.0000% 7.5013% 30.0451% 3.1738% 4.7584% 0.0000% 0.0000%   \n",
       "\n",
       "      TGT     TMO    TTWO  \n",
       "0 2.6977% 1.2639% 1.9033%  \n",
       "1 0.0310% 7.6685% 3.8109%  \n",
       "2 0.0000% 9.6204% 4.5798%  \n",
       "3 0.0000% 9.7291% 5.7723%  \n",
       "4 0.0000% 8.3937% 7.7205%  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "points = 40 # Number of points of the frontier\n",
    "\n",
    "frontier = port.efficient_frontier(model=model, rm=rm, kelly=\"exact\", points=points, rf=rf, hist=hist)\n",
    "\n",
    "display(frontier.T.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x314224790>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the efficient frontier\n",
    "\n",
    "label = 'Max Risk Adjusted Log Return Portfolio' # Title of point\n",
    "mu = port.mu # Expected returns\n",
    "cov = port.cov # Covariance matrix\n",
    "returns = port.returns # Returns of the assets\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "rp.plot_frontier(w_frontier=frontier,\n",
    "                 mu=mu,\n",
    "                 cov=cov,\n",
    "                 returns=returns,\n",
    "                 rm=rm,\n",
    "                 kelly=True,\n",
    "                 rf=rf,\n",
    "                 alpha=0.05,\n",
    "                 cmap='viridis',\n",
    "                 w=w_3,\n",
    "                 label=label,\n",
    "                 marker='*',\n",
    "                 s=16,\n",
    "                 c='r',\n",
    "                 height=6,\n",
    "                 width=10,\n",
    "                 t_factor=12,\n",
    "                 ax=ax)\n",
    "\n",
    "y1 = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_1.to_numpy())) * 12 \n",
    "x1 = rp.Sharpe_Risk(w_1, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05) * 12**0.5\n",
    "\n",
    "y2 = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_2.to_numpy())) * 12 \n",
    "x2 = rp.Sharpe_Risk(w_2, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05) * 12**0.5\n",
    "\n",
    "ax.scatter(x=x1,\n",
    "           y=y1,\n",
    "           marker=\"^\",\n",
    "           s=8**2,\n",
    "           c=\"b\",\n",
    "           label=\"Max Risk Adjusted Arithmetic Return Portfolio\")\n",
    "ax.scatter(x=x2,\n",
    "           y=y2,\n",
    "           marker=\"v\",\n",
    "           s=8**2,\n",
    "           c=\"c\",\n",
    "           label=\"Max Risk Adjusted Approx Log Return Portfolio\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Estimating Logarithmic Mean EVaR Portfolios\n",
    "\n",
    "### 2.1 Calculating the portfolio that maximizes Risk Adjusted Return."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Arithmetic</th>\n",
       "      <th>Log Approx</th>\n",
       "      <th>Log Exact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AIG</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AKAM</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APA</th>\n",
       "      <td>6.6632%</td>\n",
       "      <td>3.0801%</td>\n",
       "      <td>3.4319%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAX</th>\n",
       "      <td>6.0537%</td>\n",
       "      <td>2.2601%</td>\n",
       "      <td>2.3704%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BKNG</th>\n",
       "      <td>6.9015%</td>\n",
       "      <td>5.0120%</td>\n",
       "      <td>5.1704%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMY</th>\n",
       "      <td>4.7030%</td>\n",
       "      <td>6.7796%</td>\n",
       "      <td>6.8632%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMCSA</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNP</th>\n",
       "      <td>0.0844%</td>\n",
       "      <td>2.9095%</td>\n",
       "      <td>2.7665%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CPB</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MO</th>\n",
       "      <td>16.0752%</td>\n",
       "      <td>21.5159%</td>\n",
       "      <td>21.1998%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NI</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NKTR</th>\n",
       "      <td>6.8840%</td>\n",
       "      <td>7.8522%</td>\n",
       "      <td>7.7995%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTAP</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCAR</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSA</th>\n",
       "      <td>26.3938%</td>\n",
       "      <td>29.7974%</td>\n",
       "      <td>29.5636%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REGN</th>\n",
       "      <td>13.6192%</td>\n",
       "      <td>4.3211%</td>\n",
       "      <td>4.8973%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SBAC</th>\n",
       "      <td>5.1955%</td>\n",
       "      <td>5.4978%</td>\n",
       "      <td>5.4918%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEE</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.2065%</td>\n",
       "      <td>0.0123%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TGT</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TMO</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTWO</th>\n",
       "      <td>7.4265%</td>\n",
       "      <td>10.7677%</td>\n",
       "      <td>10.4332%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Arithmetic  Log Approx  Log Exact\n",
       "AIG       0.0000%     0.0000%    0.0000%\n",
       "AKAM      0.0000%     0.0000%    0.0000%\n",
       "AMT       0.0000%     0.0000%    0.0000%\n",
       "APA       6.6632%     3.0801%    3.4319%\n",
       "BA        0.0000%     0.0000%    0.0000%\n",
       "BAX       6.0537%     2.2601%    2.3704%\n",
       "BKNG      6.9015%     5.0120%    5.1704%\n",
       "BMY       4.7030%     6.7796%    6.8632%\n",
       "CMCSA     0.0000%     0.0000%    0.0000%\n",
       "CNP       0.0844%     2.9095%    2.7665%\n",
       "CPB       0.0000%     0.0000%    0.0000%\n",
       "DE        0.0000%     0.0000%    0.0000%\n",
       "MO       16.0752%    21.5159%   21.1998%\n",
       "MSFT      0.0000%     0.0000%    0.0000%\n",
       "NI        0.0000%     0.0000%    0.0000%\n",
       "NKTR      6.8840%     7.8522%    7.7995%\n",
       "NTAP      0.0000%     0.0000%    0.0000%\n",
       "PCAR      0.0000%     0.0000%    0.0000%\n",
       "PSA      26.3938%    29.7974%   29.5636%\n",
       "REGN     13.6192%     4.3211%    4.8973%\n",
       "SBAC      5.1955%     5.4978%    5.4918%\n",
       "SEE       0.0000%     0.0000%    0.0000%\n",
       "T         0.0000%     0.2065%    0.0123%\n",
       "TGT       0.0000%     0.0000%    0.0000%\n",
       "TMO       0.0000%     0.0000%    0.0000%\n",
       "TTWO      7.4265%    10.7677%   10.4332%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rm = 'EVaR' # Risk measure\n",
    "\n",
    "w_1 = port.optimization(model=model, rm=rm, obj=obj, kelly=False, rf=rf, l=l, hist=hist)\n",
    "w_2 = port.optimization(model=model, rm=rm, obj=obj, kelly='approx', rf=rf, l=l, hist=hist)\n",
    "w_3 = port.optimization(model=model, rm=rm, obj=obj, kelly='exact', rf=rf, l=l, hist=hist)\n",
    "\n",
    "w = pd.concat([w_1, w_2, w_3], axis=1)\n",
    "w.columns = ['Arithmetic', 'Log Approx', 'Log Exact']\n",
    "\n",
    "display(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14,6))\n",
    "w.plot(kind='bar', ax = ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Risk Adjusted Return:\n",
      "Arithmetic 0.6328895917031077\n",
      "Log Approx 0.646264410360255\n",
      "Log Exact 0.6463392695478154\n"
     ]
    }
   ],
   "source": [
    "returns = port.returns\n",
    "cov = port.cov\n",
    "\n",
    "y = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_1.to_numpy()))\n",
    "x = rp.Sharpe_Risk(w_1, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05)\n",
    "print(\"Risk Adjusted Return:\")\n",
    "print(\"Arithmetic\", (y/x).item() * 12**0.5)\n",
    "\n",
    "y = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_2.to_numpy()))\n",
    "x = rp.Sharpe_Risk(w_2, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05)\n",
    "print(\"Log Approx\", (y/x).item() * 12**0.5)\n",
    "\n",
    "y = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_3.to_numpy()))\n",
    "x = rp.Sharpe_Risk(w_3, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05)\n",
    "print(\"Log Exact\", (y/x).item() * 12**0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Calculate efficient frontier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AIG</th>\n",
       "      <th>AKAM</th>\n",
       "      <th>AMT</th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BKNG</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>...</th>\n",
       "      <th>NTAP</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>REGN</th>\n",
       "      <th>SBAC</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TTWO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.8287%</td>\n",
       "      <td>2.3020%</td>\n",
       "      <td>18.2686%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.9355%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>17.2219%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.2723%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.7947%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>7.5239%</td>\n",
       "      <td>8.0870%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.4480%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.8679%</td>\n",
       "      <td>3.1557%</td>\n",
       "      <td>11.5998%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.8139%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>25.3001%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>7.1302%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.4124%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>11.6057%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.2820%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.8146%</td>\n",
       "      <td>3.7346%</td>\n",
       "      <td>10.1901%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.6608%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>27.5502%</td>\n",
       "      <td>1.1712%</td>\n",
       "      <td>6.6494%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.7242%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>11.3770%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.9387%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.7741%</td>\n",
       "      <td>4.6238%</td>\n",
       "      <td>7.9820%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.0593%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>28.6374%</td>\n",
       "      <td>3.6377%</td>\n",
       "      <td>5.8159%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.7739%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>10.7413%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.0460%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.2112%</td>\n",
       "      <td>5.3233%</td>\n",
       "      <td>6.0734%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.5929%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>29.9376%</td>\n",
       "      <td>5.5013%</td>\n",
       "      <td>5.3980%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>10.4707%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      AIG    AKAM     AMT     APA      BA     BAX    BKNG      BMY   CMCSA  \\\n",
       "0 0.0000% 0.0000% 0.0000% 0.0000% 0.0000% 2.8287% 2.3020% 18.2686% 0.0000%   \n",
       "1 0.0000% 0.0000% 0.0000% 3.4480% 0.0000% 3.8679% 3.1557% 11.5998% 0.0000%   \n",
       "2 0.0000% 0.0000% 0.0000% 4.2820% 0.0000% 2.8146% 3.7346% 10.1901% 0.0000%   \n",
       "3 0.0000% 0.0000% 0.0000% 3.9387% 0.0000% 2.7741% 4.6238%  7.9820% 0.0000%   \n",
       "4 0.0000% 0.0000% 0.0000% 3.0460% 0.0000% 2.2112% 5.3233%  6.0734% 0.0000%   \n",
       "\n",
       "      CNP  ...    NTAP    PCAR      PSA    REGN    SBAC     SEE       T  \\\n",
       "0 1.9355%  ... 0.0000% 0.0000% 17.2219% 0.0000% 5.2723% 0.0000% 6.7947%   \n",
       "1 3.8139%  ... 0.0000% 0.0000% 25.3001% 0.0000% 7.1302% 0.0000% 4.4124%   \n",
       "2 3.6608%  ... 0.0000% 0.0000% 27.5502% 1.1712% 6.6494% 0.0000% 1.7242%   \n",
       "3 3.0593%  ... 0.0000% 0.0000% 28.6374% 3.6377% 5.8159% 0.0000% 0.7739%   \n",
       "4 2.5929%  ... 0.0000% 0.0000% 29.9376% 5.5013% 5.3980% 0.0000% 0.0000%   \n",
       "\n",
       "      TGT     TMO     TTWO  \n",
       "0 0.0000% 7.5239%  8.0870%  \n",
       "1 0.0000% 0.0000% 11.6057%  \n",
       "2 0.0000% 0.0000% 11.3770%  \n",
       "3 0.0000% 0.0000% 10.7413%  \n",
       "4 0.0000% 0.0000% 10.4707%  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "points = 40 # Number of points of the frontier\n",
    "\n",
    "frontier = port.efficient_frontier(model=model, rm=rm, kelly=\"exact\", points=points, rf=rf, hist=hist)\n",
    "\n",
    "display(frontier.T.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x31433b4d0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the efficient frontier\n",
    "\n",
    "label = 'Max Risk Adjusted Log Return Portfolio' # Title of point\n",
    "mu = port.mu # Expected returns\n",
    "cov = port.cov # Covariance matrix\n",
    "returns = port.returns # Returns of the assets\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "rp.plot_frontier(w_frontier=frontier,\n",
    "                 mu=mu,\n",
    "                 cov=cov,\n",
    "                 returns=returns,\n",
    "                 rm=rm,\n",
    "                 kelly=True,\n",
    "                 rf=rf,\n",
    "                 alpha=0.05,\n",
    "                 cmap='viridis',\n",
    "                 w=w_3,\n",
    "                 label=label,\n",
    "                 marker='*',\n",
    "                 s=16,\n",
    "                 c='r',\n",
    "                 height=6,\n",
    "                 width=10,\n",
    "                 t_factor=12,\n",
    "                 ax=ax)\n",
    "\n",
    "y1 = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_1.to_numpy())) * 12\n",
    "x1 = rp.Sharpe_Risk(w_1, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05) * 12**0.5\n",
    "\n",
    "y2 = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_2.to_numpy())) * 12\n",
    "x2 = rp.Sharpe_Risk(w_2, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05) * 12**0.5\n",
    "\n",
    "ax.scatter(x=x1,\n",
    "           y=y1,\n",
    "           marker=\"^\",\n",
    "           s=8**2,\n",
    "           c=\"b\",\n",
    "           label=\"Max Risk Adjusted Arithmetic Return Portfolio\")\n",
    "ax.scatter(x=x2,\n",
    "           y=y2,\n",
    "           marker=\"v\",\n",
    "           s=8**2,\n",
    "           c=\"c\",\n",
    "           label=\"Max Risk Adjusted Approx Log Return Portfolio\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Estimating Logarithmic Mean EDaR Portfolios\n",
    "\n",
    "### 3.1 Calculating the portfolio that maximizes Risk Adjusted Return."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Arithmetic</th>\n",
       "      <th>Log Approx</th>\n",
       "      <th>Log Exact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AIG</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AKAM</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT</th>\n",
       "      <td>2.8669%</td>\n",
       "      <td>0.3719%</td>\n",
       "      <td>0.5224%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APA</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAX</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BKNG</th>\n",
       "      <td>3.0492%</td>\n",
       "      <td>1.4544%</td>\n",
       "      <td>1.6956%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMY</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMCSA</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNP</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CPB</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MO</th>\n",
       "      <td>27.1688%</td>\n",
       "      <td>34.2880%</td>\n",
       "      <td>33.5919%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NI</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NKTR</th>\n",
       "      <td>12.5571%</td>\n",
       "      <td>9.2344%</td>\n",
       "      <td>10.2399%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTAP</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCAR</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0002%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSA</th>\n",
       "      <td>25.6704%</td>\n",
       "      <td>33.9398%</td>\n",
       "      <td>30.4417%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REGN</th>\n",
       "      <td>19.7835%</td>\n",
       "      <td>12.5676%</td>\n",
       "      <td>14.1519%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SBAC</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.8729%</td>\n",
       "      <td>3.7937%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEE</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TGT</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TMO</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0002%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTWO</th>\n",
       "      <td>8.9041%</td>\n",
       "      <td>4.2702%</td>\n",
       "      <td>5.5630%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Arithmetic  Log Approx  Log Exact\n",
       "AIG       0.0000%     0.0000%    0.0000%\n",
       "AKAM      0.0000%     0.0000%    0.0000%\n",
       "AMT       2.8669%     0.3719%    0.5224%\n",
       "APA       0.0000%     0.0000%    0.0000%\n",
       "BA        0.0000%     0.0000%    0.0000%\n",
       "BAX       0.0000%     0.0000%    0.0000%\n",
       "BKNG      3.0492%     1.4544%    1.6956%\n",
       "BMY       0.0000%     0.0000%    0.0000%\n",
       "CMCSA     0.0000%     0.0000%    0.0000%\n",
       "CNP       0.0000%     0.0000%    0.0000%\n",
       "CPB       0.0000%     0.0000%    0.0000%\n",
       "DE        0.0000%     0.0000%    0.0000%\n",
       "MO       27.1688%    34.2880%   33.5919%\n",
       "MSFT      0.0000%     0.0000%    0.0000%\n",
       "NI        0.0000%     0.0000%    0.0000%\n",
       "NKTR     12.5571%     9.2344%   10.2399%\n",
       "NTAP      0.0000%     0.0000%    0.0000%\n",
       "PCAR      0.0000%     0.0002%    0.0000%\n",
       "PSA      25.6704%    33.9398%   30.4417%\n",
       "REGN     19.7835%    12.5676%   14.1519%\n",
       "SBAC      0.0000%     3.8729%    3.7937%\n",
       "SEE       0.0000%     0.0000%    0.0000%\n",
       "T         0.0000%     0.0000%    0.0000%\n",
       "TGT       0.0000%     0.0000%    0.0000%\n",
       "TMO       0.0000%     0.0002%    0.0000%\n",
       "TTWO      8.9041%     4.2702%    5.5630%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rm = 'EDaR' # Risk measure\n",
    "\n",
    "w_1 = port.optimization(model=model, rm=rm, obj=obj, kelly=False, rf=rf, l=l, hist=hist)\n",
    "w_2 = port.optimization(model=model, rm=rm, obj=obj, kelly='approx', rf=rf, l=l, hist=hist)\n",
    "w_3 = port.optimization(model=model, rm=rm, obj=obj, kelly='exact', rf=rf, l=l, hist=hist)\n",
    "\n",
    "w = pd.concat([w_1, w_2, w_3], axis=1)\n",
    "w.columns = ['Arithmetic', 'Log Approx', 'Log Exact']\n",
    "\n",
    "display(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14,6))\n",
    "w.plot(kind='bar', ax = ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Risk Adjusted Return:\n",
      "Arithmetic 0.8286208015351624\n",
      "Log Approx 0.8435804152316564\n",
      "Log Exact 0.8449872652743937\n"
     ]
    }
   ],
   "source": [
    "returns = port.returns\n",
    "cov = port.cov\n",
    "\n",
    "y = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_1.to_numpy()))\n",
    "x = rp.Sharpe_Risk(w_1, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05)\n",
    "print(\"Risk Adjusted Return:\")\n",
    "print(\"Arithmetic\", (y/x).item() * 12)\n",
    "\n",
    "y = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_2.to_numpy()))\n",
    "x = rp.Sharpe_Risk(w_2, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05)\n",
    "print(\"Log Approx\", (y/x).item() * 12)\n",
    "\n",
    "y = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_3.to_numpy()))\n",
    "x = rp.Sharpe_Risk(w_3, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05)\n",
    "print(\"Log Exact\", (y/x).item() * 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Calculate efficient frontier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AIG</th>\n",
       "      <th>AKAM</th>\n",
       "      <th>AMT</th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BKNG</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>...</th>\n",
       "      <th>NTAP</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>REGN</th>\n",
       "      <th>SBAC</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TTWO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.9630%</td>\n",
       "      <td>5.5773%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.9599%</td>\n",
       "      <td>2.3312%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>23.2474%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.5704%</td>\n",
       "      <td>3.6697%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.9192%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>31.4908%</td>\n",
       "      <td>5.8685%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.2750%</td>\n",
       "      <td>1.2159%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.3203%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>36.5054%</td>\n",
       "      <td>8.0957%</td>\n",
       "      <td>0.4379%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.7604%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.6926%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.2423%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>34.9683%</td>\n",
       "      <td>12.1345%</td>\n",
       "      <td>2.4519%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.5031%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.8849%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.8560%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>27.4039%</td>\n",
       "      <td>15.3671%</td>\n",
       "      <td>3.4524%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.5587%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      AIG    AKAM     AMT     APA      BA     BAX    BKNG     BMY   CMCSA  \\\n",
       "0 0.0000% 0.0000% 1.9630% 5.5773% 0.0000% 0.0000% 1.9599% 2.3312% 0.0000%   \n",
       "1 0.0000% 0.0000% 2.5704% 3.6697% 0.0000% 0.0000% 0.9192% 0.0000% 0.0000%   \n",
       "2 0.0000% 0.0000% 3.2750% 1.2159% 0.0000% 0.0000% 0.3203% 0.0000% 0.0000%   \n",
       "3 0.0000% 0.0000% 1.6926% 0.0000% 0.0000% 0.0000% 1.2423% 0.0000% 0.0000%   \n",
       "4 0.0000% 0.0000% 0.8849% 0.0000% 0.0000% 0.0000% 1.8560% 0.0000% 0.0000%   \n",
       "\n",
       "      CNP  ...    NTAP    PCAR      PSA     REGN    SBAC     SEE       T  \\\n",
       "0 0.0000%  ... 0.0000% 0.0000% 23.2474%  0.0000% 0.0000% 0.0000% 0.0000%   \n",
       "1 0.0000%  ... 0.0000% 0.0000% 31.4908%  5.8685% 0.0000% 0.0000% 0.0000%   \n",
       "2 0.0000%  ... 0.0000% 0.0000% 36.5054%  8.0957% 0.4379% 0.0000% 0.0000%   \n",
       "3 0.0000%  ... 0.0000% 0.0000% 34.9683% 12.1345% 2.4519% 0.0000% 0.0000%   \n",
       "4 0.0000%  ... 0.0000% 0.0000% 27.4039% 15.3671% 3.4524% 0.0000% 0.0000%   \n",
       "\n",
       "      TGT     TMO    TTWO  \n",
       "0 0.0000% 0.0000% 0.0000%  \n",
       "1 0.0000% 0.0000% 0.0000%  \n",
       "2 0.0000% 0.0000% 0.7604%  \n",
       "3 0.0000% 0.0000% 3.5031%  \n",
       "4 0.0000% 0.0000% 6.5587%  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "points = 40 # Number of points of the frontier\n",
    "\n",
    "frontier = port.efficient_frontier(model=model, rm=rm, kelly=\"exact\", points=points, rf=rf, hist=hist)\n",
    "\n",
    "display(frontier.T.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x31c2d2450>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the efficient frontier\n",
    "\n",
    "label = 'Max Risk Adjusted Log Return Portfolio' # Title of point\n",
    "mu = port.mu # Expected returns\n",
    "cov = port.cov # Covariance matrix\n",
    "returns = port.returns # Returns of the assets\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "rp.plot_frontier(w_frontier=frontier,\n",
    "                 mu=mu,\n",
    "                 cov=cov,\n",
    "                 returns=returns,\n",
    "                 rm=rm,\n",
    "                 kelly=True,\n",
    "                 rf=rf,\n",
    "                 alpha=0.05,\n",
    "                 cmap='viridis',\n",
    "                 w=w_3,\n",
    "                 label=label,\n",
    "                 marker='*',\n",
    "                 s=16,\n",
    "                 c='r',\n",
    "                 height=6,\n",
    "                 width=10,\n",
    "                 t_factor=12,\n",
    "                 ax=ax)\n",
    "\n",
    "y1 = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_1.to_numpy())) * 12\n",
    "x1 = rp.Sharpe_Risk(w_1, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05)\n",
    "\n",
    "y2 = 1/(returns.shape[0]) * np.sum(np.log(1 + returns @ w_2.to_numpy())) * 12\n",
    "x2 = rp.Sharpe_Risk(w_2, cov=cov, returns=returns, rm=rm, rf=rf, alpha=0.05)\n",
    "\n",
    "ax.scatter(x=x1,\n",
    "           y=y1,\n",
    "           marker=\"^\",\n",
    "           s=8**2,\n",
    "           c=\"b\",\n",
    "           label=\"Max Risk Adjusted Arithmetic Return Portfolio\")\n",
    "ax.scatter(x=x2,\n",
    "           y=y2,\n",
    "           marker=\"v\",\n",
    "           s=8**2,\n",
    "           c=\"c\",\n",
    "           label=\"Max Risk Adjusted Approx Log Return Portfolio\")\n",
    "plt.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
